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CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 163卷

作者： Rudi, Johann Lee, Youngjun Chadha, Aidan H. Wahib, Mohamed Weide, Klaus O'Neal, Jared P. Dubey, Anshu Virginia Tech Dept Math 225 Stanger St Blacksburg VA 24061 USA Argonne Natl Lab Math & Comp Sci Div 9700 S Cass Ave Lemont IL 60439 USA RIKEN Ctr Computat Sci 7-1-26 Minatojima Minami Machi Kobe 6500047 Japan Univ Chicago Dept Comp Sci 5730 South Ellis Ave Chicago IL 60637 USA

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle

关键词： Code generation Performance portability Algorithmic portability Syntax tree control flow graph Multiphysics simulation

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2015年第7期25卷 1171-1197页

作者： Qiu, Dehong Sun, Jialin Li, Hao Huazhong Univ Sci & Technol Sch Software Engn Wuhan 430074 Hubei Province Peoples R China

Measuring program similarity plays an important role in solving many problems in software engineering. However, because programs are instruction sequences with complex structures and semantic functions and furthermore, programs may be obfuscated deliberately through semantics-preserving transformations, measuring program similarity is a difficult task that has not been adequately addressed. In this paper, we propose a new approach to measuring Java program similarity. The approach first measures the low-level similarity between basic blocks according to the bytecode instruction sequences and the structural property of the basic blocks. Then, an error-tolerant graph matching algorithm that can combat structure transformations is used to match the control flow graphs (CFG) based on the basic block similarity. The high-level similarity between Java programs is subsequently calculated on the matched pairs of the independent paths extracted from the optimal CFG matching. The proposed CFG-Match approach is compared with a string-based approach, a tree-based approach and a graph-based approach. Experimental results show that the CFG-Match approach is more accurate and robust against semantics-preserving transformations. The CFG-Match approach is used to detect Java program plagiarism. Experiments on the collection of benchmark program pairs collected from the students' submission of project assignments demonstrate that the CFG-Match approach outperforms the comparative approaches in the detection of Java program plagiarism.

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A Combination Method for Android Malware Detection Based on control flow graphs and Machine Learning Algorithms

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IEEE ACCESS 2019年 7卷 21235-21245页

作者： Ma, Zhuo Ge, Haoran Liu, Yang Zhao, Meng Ma, Jianfeng Xidian Univ Sch Cyber Engn Xian 710071 Shaanxi Peoples R China Shaanxi Key Lab Network & Syst Secur Xian 710071 Shaanxi Peoples R China

Android malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware. In this paper, we present a combination method for Android malware detection based on the machine learning algorithm. First, we construct the control flow graph of the application to obtain API information. Based on the API information, we innovatively construct Boolean, frequency, and time-series data sets. Based on these three data sets, three detection models for Android malware detection regarding API calls, API frequency, and API sequence aspects are constructed. Ultimately, an ensemble model is constructed for conformity. We tested and compared the accuracy and stability of our detection models through a large number of experiments. The experiments were conducted on 10010 benign applications and 10683 malicious applications. The results show that our detection model achieves 98.98% detection precision and has high accuracy and stability. All of the results are consistent with the theoretical analysis in this paper.

关键词： control flow graph application programming interface machine learning malware detection

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Information entropy applied to software based control flow graphs

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2018年第5期9卷 1080-1091页

作者： Akundi, Aditya Smith, Eric Tseng, Tzu-Liang Univ Texas El Paso Ind Mfg & Syst Engn Dept El Paso TX 79968 USA

Information theory, introduced by Shannon in the context of information transfer in communication channels, is used as a foundation for research in many diverse fields. The concept Entropy in terms of information theory is seen as the average amount of information or the rate of information produced when forming a message, element by element. Entropy has found broad application in many research fields and can also be applied in software engineering for quantifying the uncertainty associated with a software code. In this paper, information entropy and its application towards measuring software complexity are explored, along with the formulation of an information entropy based complexity measure that considers logical decision-making, processes, and software statement interaction patterns in control flow graphs mapped from actual software code. To broaden the application of the proposed metric, the execution times of nodes in the control flow graphs are also incorporated. Further, the metric is evaluated against eight different axioms that a software complexity measure should satisfy.

关键词： Complexity control flow graph Entropy Information theory Software complexity

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Practical dynamic reconstruction of control flow graphs

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SOFTWARE-PRACTICE & EXPERIENCE 2021年第2期51卷 353-384页

作者： Rimsa, Andrei Amaral, Jose Nelson Pereira, Fernando M. Q. CEFET MG Dept Comp Av Amazonas 7675 BR-30510000 Belo Horizonte MG Brazil Univ Alberta Dept Comp Sci Edmonton AB Canada Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil

The automatic recovery of a program's high-level representation from its binary version is a well-studied problem in programming languages. However, most of the solutions to this problem are based on purely static approaches: techniques such as dataflow analyses or type inference are used to convert the bytes that constitute the executable code back into a control flow graph (CFG). This article departs from such a modus operandi to show that a dynamic analysis can be effective and useful, both as a standalone technique, and as a way to enhance the precision of static approaches. The experimental results provide evidence that completeness, that is, the ability to conclude that the entire CFG has been discovered, is achievable on many functions that are part of industry-strong benchmarks. Experiments also indicate that dynamic information greatly enhances the ability ofDynInst, a state-of-the-art binary reconstructor, to deal with code stripped of debugging information. These results were obtained withCFGgrind, a new implementation of a dynamic code reconstructor, built on top ofValgrind. When applied tocBench,CFGgrindis 9% faster thancallgrind,Valgrind's tool used to track targets of function calls;and 7% faster inSpec *** the complete CFG of 40% of all the procedures invoked during the standard execution of programs inSpec Cpu2017, and 37% incBench. When combined withCFGgrind,DynInstfinds 15% more CFGs forcBench, and 7% more CFGs forSpec Cpu2017. Finally,CFGgrindis more than 7 times faster than DCFG, a CFG reconstructor from Intel, and 1.30 times faster thanbfTrace, a CFG reconstructor used in *** also more precise than these two tools, handling operating system signals, shared code in functions, and unaligned instructions;besides supporting multithreaded programs, exact profiling and incremental refinements.

关键词： code instrumentation control flow graph dynamic analysis

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Generating Customised control flow graphs for Legacy Languages with Semi-Parsing 39

Generating Customised Control Flow Graphs for Legacy Languag...

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39th IEEE International Conference on Software Maintenance and Evolution (ICSME)

作者： Deknop, Celine Fabry, Johan Mens, Kim Zaytsev, Vadim UCLouvain ICTEAM Inst Ottignies Belgium Raincode Labs Brussels Belgium UTwente Formal Methods & Amp Tools Enschede Netherlands

ISBN: (数字)9781665479561

ISBN: (纸本)9781665479561

We propose a tool and underlying technique that uses semi-parsing to extract control flow graphs from legacy source code (i.e., COBOL). Obtaining such control flow graphs is relevant in the industrial setting of legacy modernisation, to quickly demonstrate to code owners that modernisation engineers did not break their business logic. They need to be convinced that a migration did not affect the flow around critical parts of their code such as database accesses. Focusing on the control flow around embedded SQL queries and confirming that the code logic has been preserved improves customers' trust and satisfaction in the modernisation. Our proposed algorithm and approach uses fuzzy parsing as opposed to full parsing to parse mainly the control flow constructs, while delegating the full parsing of embedded languages like SQL to an external parser, and produces a control flow graph directly while skipping over most of the input in linear time. Such a fuzzy parser is easier to construct and adapt to particular languages and needs than a full parser with a visitor to elicit control flow. Comparisons are made of the fuzzy parser to an industrial-strength full parser.

关键词： Semi-parsing fuzzy parsing legacy modernisation control flow graph COBOL SQL industrial use case

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Malware Classification by Learning Semantic and Structural Features of control flow graphs 20

Malware Classification by Learning Semantic and Structural F...

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20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Wu, Bolun Xu, Yuanhang Zou, Futai Shanghai Jiao Tong Univ Sch Cyber Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781665416580

Malware has become one of the biggest threats in the cyber world due to its ever-evolving nature. Machine learning-based methods rely on expertise in selecting handcrafted features which is time-consuming. Recent control flow graphs (CFGs) based method makes the best of graph neural network (GNN) on malware classification. But it ignores the semantic information inside CFGs and also relies on manually-designed features. To overcome these drawbacks, in this work, we introduce a malware classification model called MCBG that applies BERT and graph Isomorphism Network with JumpingKnowledge (GIN-JK) to capture both semantic and structural features of CFGs. We conduct 5-fold cross-validation on Microsoft Malware Classification Challenge dataset to evaluate our model and it achieves an accuracy of 99.53%. The experimental results demonstrate that building semantic models on basic blocks is essential. Besides, MCBG outperforms the model which only considers CFG's structural information. Also, without using any manually-selected feature, it outperforms those of the state-of-the-art methods based on handcrafted malware features.

关键词： malware classification control flow graph deep learning

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7th International Conference on Engineering and Emerging Technologies (ICEET)

作者： Desku, Astrit Raufi, Bujar Luma, Artan Selimi, Besnik South East European Univ Fac Contemporary Sci & Technol Tetovo North Macedonia

ISBN: (纸本)9781665427142

As the recommender systems need data to generate a recommendation, the dataset is intended as a standardized input for research on recommendation systems for software engineering but is also useful in many other areas that analyze source code. In this paper, we present a collection of 557-cryptography C# solutions, which includes 19590 classes. From these repositories of classes are generated two datasets with 19,590 lines of code, respectively 1,191,340 lines of code using control flow graph rules. For two datasets are calculated TF-IDF and Cosine Similarity.

关键词： Recommender Systems Machine Learning control flow graph TF-IDF Cosine Similarity

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Creating and manipulating control flow graphs with multilevel grouping and code coverage

Creating and manipulating control flow graphs with multileve...

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8th International Conference on Enterprise Information Systems (ICEIS 2006)

作者： Sofokleous, Anastasis A. Andreou, Andreas S. Iaokim, Gianna Brunel Univ Uxbridge UB8 3PH Middx England Univ Cyprus Nicosia Cyprus

ISBN: (纸本)9728865414

Various researchers and practitioners have proposed the use of control flow graphs for investigating software engineering aspects, such as testing, slicing, program analysis and debugging. However, the relevant software applications support only low level languages (e.g. C, C++) and most, if not all, of the research papers do not provide information or any facts showing the tool implementation for the control flow graph, leaving it to the reader to imagine either that the author is using third party software for creating the graph, or that the graph is constructed manually (by band). In this paper, we extend our previous work on a dedicated program analysis architecture and we describe a tool for automatic production of the control flow graph that offers advanced capabilities, such as vertices grouping, code coverage and enhanced user interaction.

关键词： control flow graph node grouping code coverage

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Test Case Selection Technique for Regression Testing Using Differential control flow graphs 16

Test Case Selection Technique for Regression Testing Using D...

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16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

作者： Akimoto, Shun Yaegashi, Rihito Takagi, Tomohiko Kagawa Univ Grad Sch Engn Takamatsu Kagawa Japan Kagawa Univ Fac Engn Takamatsu Kagawa Japan

ISBN: (纸本)9781479986767

This paper shows a new technique to select effective test cases for regression testing of software. In the technique, an UCN (update complexity number) of each module of software under test is calculated based on a differential control flow graph, and then test cases that execute modules with larger UCNs are selected automatically.

关键词： software testing regression testing test case structured chart control flow graph

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